Author

S. Khamzin

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Physiology· 2 Biophysics· 1

Signature Assays, Organisms & Methods

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Organism:human· 2 Approach:statistical model· 2 Research Focus:cell signaling & trafficking· 1 Assay:clinical cohort / trial· 1 Research Focus:drug response / drug discovery· 1 Assay:electrophysiology· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

O. Solovyova· 3 shared A. Dokuchaev· 2 shared V. Gurev· 2 shared A. Bazhutina· 1 shared D. Lebedev· 1 shared J. Kozloski· 1 shared J. Parikh· 1 shared P. Di Achille· 1 shared

Activity by Year

2019-2021 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 30 mutual co-author links

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3 shared papersPhysiology

O. Solovyova

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Author profile →

3 papers

Recent·Most cited

Machine Learning prediction of cardiac resynchronisation therapy response from combination of clinical and model-driven data

S. Khamzin, A. Dokuchaev, A. Bazhutina, T. Chumarnaya, S. Zubarev, T. Lyubimtseva, V. Lebedeva, D. Lebedev, V. Gurev, O. Solovyova

This study develops a machine learning model to predict cardiac resynchronization therapy (CRT) response by combining clinical data with model-driven biomarkers from personalized cardiac simulations.

Frontiers in Physiology · Dec 14, 2021Peer-reviewedPhysiology

A Population Modelling Approach to Studying Age-Related Effects on Excitation-Contraction Coupling in Human Cardiomyocytes

A. Dokuchaev, S. Khamzin, O. Solovyova

The paper uses a population modelling approach to study age-related effects on excitation-contraction coupling in human cardiomyocytes, finding that changes in CaL current have the most significant impact on arrhythmogenic risk.

bioRxiv · Dec 17, 2019 · PreprintPhysiology

Model order reduction for left ventricular mechanics via congruency training

P. Di Achille, J. Parikh, S. Khamzin, O. Solovyova, J. Kozloski, V. Gurev

This paper introduces a model order reduction strategy for 3-D finite element models of ventricular mechanics using congruency training and linear transformations to improve computational efficiency.

bioRxiv · Jul 10, 2019Biophysics